# Predictive inference with the jackknife+

## TL;DR

The paper introduces the jackknife+ method for constructing predictive confidence intervals with guaranteed coverage, improving upon the original jackknife by accounting for variability in predictions, and extends it to K-fold cross-validation.

## Contribution

The paper proposes the jackknife+ method, providing rigorous coverage guarantees for predictive intervals and extending it to K-fold cross-validation, with theoretical and empirical validation.

## Key findings

- Jackknife+ achieves nearly exact coverage.
- Intervals are similar in length to the original jackknife under stability.
- Coverage guarantees hold regardless of data distribution.

## Abstract

This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to K-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk [2015] and we discuss connections.